基于聚类分析和混合自适应进化算法的短期风电功率预测

Short-term wind power forecasting based on cluster analysis and a hybrid evolutionary-adaptive methodology

针对传统风电功率预测方法难以满足精细化、动态化建模要求,存在易陷入局部最优等问题,提出了基于聚类分析和混合自适应进化算法(KHEA)的风电功率智能预测方法。首先,采用K均值聚类算法对全年风速和功率数据进行聚类,剔除不合理的数据。然后,采用小波变换(WT)识别功率数据的行为特征,获得解构序列集,进而建立BP神经网络模型对未来时间段的功率解构序列进行预测。为减少预测误差,采用进化粒子群算法(EPSO)对模型的权值和阈值进行调整和优化,实现EPSO进化特性与神经网络自学习能力的功能互补。最后,运用逆小波变换对预测序列进行重构,获得最终的功率预测值。运用中国南方某风电场数据开展仿真实验,并与其他模型进行对比,表明KHEA具有更高的风电功率短期预测精度和可靠性,为提高风电功率预测精度和优化调度管理提供了新的技术方案。

It is difficult for traditional wind power forecasting methods to meet the requirements of refined and dynamic modeling.It is also easy for them to fall into local optimality.Thus an intelligent wind power forecasting method based on cluster analysis and a Hybrid Adaptive Evolution Algorithm(KHEA)is proposed.First,the K-means clustering algorithm is used to cluster the annual wind speed and power data to eliminate unreasonable data.Then,Wavelet Transform(WT)is used to identify the behavioral characteristics of the power data,and the set of deconstructed sequences is obtained.Then a BP neural network model is established to predict the power deconstructed sequence in a future time period.In order to reduce the prediction error,the Evolutionary Particle Swarm Optimization(EPSO)is used to adjust and optimize the weights and thresholds of the model,so as to realize the function complementation of EPSO evolution characteristics and neural network self-learning capabilities.Finally,the inverse wavelet transform is used to reconstruct the prediction sequence to obtain the final power prediction value.Using the data of a wind farm in southern China to carry out simulation experiments,and comparing with other models,it shows that KHEA has higher wind power short-term prediction accuracy and reliability,and provides new technical solutions for improving wind power prediction accuracy and optimizing dispatch management.